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Record W2335659673 · doi:10.14796/jwmm.r206-05

Numerical Techniques for Overland Flow from Pavement

2000· article· en· W2335659673 on OpenAlexaffvenue
William James, Stuart C. Wylie

Bibliographic record

VenueJournal of Water Management Modeling · 2000
Typearticle
Languageen
FieldEngineering
TopicHydraulic flow and structures
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsFlow (mathematics)Surface runoffGeologyComputer scienceMechanicsGeotechnical engineeringPhysicsEcology

Abstract

fetched live from OpenAlex

In this chapter we discuss the fundamental processes of flow over pavement, and how they may be solved numerically.The discussion reveals additionally, a flow in the recession curve over and above that normally considered.This additional flow is similar to the anomalous pip and called the anomalous hump.Hyperbolic equations, in which category the dynamic wave equations fall, are those for which (b 2 -4 ac) is greater than zero in the general equation for any dependent variable U:ax 2 ax at iH 2 ax at Such phenomena are characterized by disturbances that propagate seemingly uncontrollably away from a stationary observer.In the case where (b 2 -4ac) equals zero, the equation i;;; said to be parabolic, and when (b 2 -4 ac) is less than zero, elliptic.Parabolic equations describe processes that are slow and damped, like groundwater flow.Elliptical equations describe steady-state problems.These coefficients appear also in equation (5.2) whose roots are the slopes ofthe characteristic curves when the partial differential equation is reduced to one involving total differentials only:- -----------------------

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.204
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2000
Admission routes2
Has abstractyes

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